SOTAVerified

Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 951–975 of 2050 papers

TitleStatusHype
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis—0
A study on the distribution of social biases in self-supervised learning visual models—0
Gaussian Process-based Spatial Reconstruction of Electromagnetic fields—0
Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data—0
Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae—0
Ensemble Method for Estimating Individualized Treatment Effects—0
Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation—0
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition—0
Efficient Distributed DNNs in the Mobile-edge-cloud Continuum—0
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
Invariance Learning in Deep Neural Networks with Differentiable Laplace ApproximationsCode1
Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks—0
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
Multi-Objective Model Selection for Time Series Forecasting—0
Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold RegressionCode0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments—0
Fitting Sparse Markov Models to Categorical Time Series Using Regularization—0
Loss-guided Stability Selection—0
Dependence model assessment and selection with DecoupleNets—0
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing dataCode1
Discovering Distribution Shifts using Latent Space RepresentationsCode0
Show:102550
← PrevPage 39 of 82Next →

No leaderboard results yet.